3.1 Summary

Let’s check how many rows and columns our dataset have, as well as check the summary of all the variables:

dim(df)

## [1] 1551505       8

summary(df)

##                Start.Time                     End.Time      
##  2017-04-15 13:27:10:      7   2017-05-21 13:07:49:      8  
##  2017-05-29 15:19:36:      7   2017-06-10 13:31:26:      8  
##  2017-04-15 13:02:35:      6   2017-02-18 14:24:00:      7  
##  2017-04-26 16:07:17:      6   2017-06-02 17:16:46:      7  
##  2017-05-15 17:14:43:      6   2017-06-03 14:32:22:      7  
##  2017-05-16 17:56:13:      6   2017-06-10 18:47:17:      7  
##  (Other)            :1551467   (Other)            :1551461  
##  Trip.Duration                          Start.Station    
##  Min.   :   60.0   Streeter Dr & Grand Ave     :  36686  
##  1st Qu.:  392.0   Clinton St & Washington Blvd:  22429  
##  Median :  670.0   Lake Shore Dr & Monroe St   :  21822  
##  Mean   :  939.8   Clinton St & Madison St     :  19098  
##  3rd Qu.: 1127.0   Canal St & Adams St         :  18105  
##  Max.   :86338.0   Lake Shore Dr & North Blvd  :  17729  
##                    (Other)                     :1415636  
##                        End.Station           User.Type      
##  Streeter Dr & Grand Ave     :  39537   Customer  : 317162  
##  Clinton St & Washington Blvd:  21113   Dependent :      4  
##  Clinton St & Madison St     :  20969   Subscriber:1234339  
##  Lake Shore Dr & Monroe St   :  20652                       
##  Lake Shore Dr & North Blvd  :  19734                       
##  Theater on the Lake         :  18529                       
##  (Other)                     :1410971                       
##     Gender         Birth.Year    
##        :316867   Min.   :1899    
##  Female:298784   1st Qu.:1975    
##  Male  :935854   Median :1984    
##                  Mean   :1981    
##                  3rd Qu.:1989    
##                  Max.   :2016    
##                  NA's   :316683

Our dataframe has around 1,5M rows and 8 columns. The variables goes as following:

• Start.Time = timestamp when the user starts a ride
• End.Time = timestamp when the user finishes a ride
• Trip.Duration = for how long the user rides the rented bike (in seconds)
• Start.Station = station where the user got the bike
• End.Station = station where the user left the bike
• User.Type = can be customer, dependent or subscriber
• Gender
• Birth.Year

Let’s take a look at the first 20 rows of the dataset:

head(df,20)

##             Start.Time            End.Time Trip.Duration
## 1  2017-01-01 00:00:36 2017-01-01 00:06:32           356
## 2  2017-01-01 00:02:54 2017-01-01 00:08:21           327
## 3  2017-01-01 00:06:06 2017-01-01 00:18:31           745
## 4  2017-01-01 00:07:28 2017-01-01 00:12:51           323
## 5  2017-01-01 00:07:57 2017-01-01 00:20:53           776
## 6  2017-01-01 00:10:44 2017-01-01 00:21:27           643
## 7  2017-01-01 00:11:34 2017-01-01 00:23:47           733
## 8  2017-01-01 00:14:57 2017-01-01 00:26:22           685
## 9  2017-01-01 00:15:03 2017-01-01 00:26:28           685
## 10 2017-01-01 00:17:01 2017-01-01 00:29:49           768
## 11 2017-01-01 00:17:13 2017-01-01 11:03:34         38781
## 12 2017-01-01 00:18:28 2017-01-01 00:31:05           757
## 13 2017-01-01 00:18:50 2017-01-01 00:21:47           177
## 14 2017-01-01 00:23:41 2017-01-01 00:29:13           332
## 15 2017-01-01 00:25:47 2017-01-01 00:39:53           846
## 16 2017-01-01 00:25:47 2017-01-01 00:43:23          1056
## 17 2017-01-01 00:26:21 2017-01-01 00:39:40           799
## 18 2017-01-01 00:27:21 2017-01-01 00:42:59           938
## 19 2017-01-01 00:27:28 2017-01-01 00:42:44           916
## 20 2017-01-01 00:27:45 2017-01-01 00:31:13           208
##                           Start.Station                   End.Station
## 1                  Canal St & Taylor St      Canal St & Monroe St (*)
## 2            Larrabee St & Menomonee St  Sheffield Ave & Kingsbury St
## 3  Orleans St & Chestnut St (NEXT Apts)    Ashland Ave & Blackhawk St
## 4               Franklin St & Monroe St        Clinton St & Tilden St
## 5                  Broadway & Barry Ave       Sedgwick St & North Ave
## 6                  State St & Kinzie St            Wells St & Polk St
## 7                Wabash Ave & Wacker Pl        Clinton St & Tilden St
## 8                    Daley Center Plaza      Canal St & Monroe St (*)
## 9                    Daley Center Plaza      Canal St & Monroe St (*)
## 10                Dayton St & North Ave       Ogden Ave & Chicago Ave
## 11           Wilton Ave & Diversey Pkwy   Halsted St & Wrightwood Ave
## 12                Canal St & Madison St      LaSalle St & Illinois St
## 13                  Theater on the Lake Lakeview Ave & Fullerton Pkwy
## 14              Halsted St & Maxwell St          Halsted St & 18th St
## 15        Ravenswood Ave & Lawrence Ave    Clarendon Ave & Gordon Ter
## 16             Clark St & Congress Pkwy         Wolcott Ave & Polk St
## 17        Ravenswood Ave & Lawrence Ave    Clarendon Ave & Gordon Ter
## 18                      Millennium Park        Michigan Ave & 18th St
## 19                      Millennium Park        Michigan Ave & 18th St
## 20              Damen Ave & Chicago Ave       Damen Ave & Division St
##     User.Type Gender Birth.Year
## 1    Customer                NA
## 2  Subscriber   Male       1984
## 3  Subscriber   Male       1985
## 4  Subscriber   Male       1990
## 5  Subscriber   Male       1990
## 6  Subscriber   Male       1970
## 7  Subscriber   Male       1986
## 8    Customer                NA
## 9    Customer                NA
## 10   Customer                NA
## 11 Subscriber Female       1988
## 12   Customer                NA
## 13 Subscriber   Male       1991
## 14 Subscriber   Male       1984
## 15 Subscriber Female       1987
## 16 Subscriber   Male       1984
## 17 Subscriber   Male       1987
## 18 Subscriber   Male       1991
## 19 Subscriber Female       1990
## 20 Subscriber   Male       1982

3.2 Start Time

How are the rides distributed along each day of the week? Is there a difference between weekdays and weekends?

df$day <- weekdays(as.Date(df$Start.Time))

grouped_per_weekday <- df %>%
              group_by(day) %>%
              summarise(count_trip_duration = round(length(Trip.Duration),0))

ggplot(grouped_per_weekday, aes(x = factor(day,c('Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday')), 
                           y = count_trip_duration)) + geom_col() + 
  geom_text(aes(label=count_trip_duration), position=position_dodge(width=0.9), vjust=-0.25) + ggtitle("Count of Trips per Weekday") + xlab("Number of Trips") + ylab("Weekday")

grouped_per_weekday <- df %>%
              group_by(day) %>%
              summarise(mean_trip_duration = round(mean(Trip.Duration),0))

ggplot(grouped_per_weekday, aes(x = factor(day,c('Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday')), 
                           y = mean_trip_duration)) + geom_col() + 
  geom_text(aes(label=mean_trip_duration), position=position_dodge(width=0.9), vjust=-0.25) + ggtitle("Average Trip Duration (in seconds) per Weekday") + xlab("Average Trip Duration") + ylab("Weekday")

Although there are slightly fewer bike trips during the weekend, the trip duration are considerably higher when compared to week days.

3.3 Trip duration

ggplot(df, aes(Trip.Duration)) + geom_histogram() + ggtitle("Histogram for Trip Duration")

Since the trip duration data is over-dispersed, let’s apply a logarithm and a square root transformation on it.

p1 <- ggplot(aes(x = Trip.Duration), data = df) + geom_histogram() + ggtitle("Histogram for Trip Duration")
p2 <- p1 + scale_x_log10() + ggtitle("Histogram for Trip Duration with Log10 normalization")
p3 <- p1 + scale_x_sqrt() + ggtitle("Histogram for Trip Duration with Sqrt normalization")

grid.arrange(p1,p2,p3,ncol=1)

3.4 Gender

Let’s check how many male riders we have:

count(df[df$Gender == 'Male',])

## # A tibble: 1 x 1
##        n
##    <int>
## 1 935854

What about female riders?

count(df[df$Gender == 'Female',])

## # A tibble: 1 x 1
##        n
##    <int>
## 1 298784

Let’s check how each Gender compare considering their average trip duration:

by(df$Trip.Duration,df$Gender,summary)

## df$Gender: 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      60     927    1379    1875    1962   86338 
## -------------------------------------------------------- 
## df$Gender: Female
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    60.0   398.8   648.0   783.7  1010.0 85782.0 
## -------------------------------------------------------- 
## df$Gender: Male
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    60.0   338.0   541.0   673.1   861.0 86096.0

Although there’s significantly more male riders than female ones, the female have longer trip durations on average. We can check how they compare using a frequency polygon:

ggplot(aes(x = Trip.Duration), data = subset(df, Gender %in% c('Male','Female'))) +
  geom_freqpoly(aes(color = Gender)) +
  scale_x_log10() + ggtitle("Frequency Polygon Comparing Gender based on Trip Duration")

We can see that the female normal distribution is slightly more left-skewed than the male curve, validating what we saw earlier.

3.5 Birth year

ggplot(df, aes(Birth.Year)) + geom_histogram() + ggtitle("Birth Year Histogram")

## Warning: Removed 316683 rows containing non-finite values (stat_bin).

Most users birth year falls between 1950 and 2000, with a peak around 1980-1990.

Distribution of types of user:

grouped_users <- df %>%
              group_by(User.Type) %>%
              summarise(count_trips = length(Trip.Duration))

ggplot(grouped_users, aes(User.Type,count_trips)) + geom_col() + 
  geom_text(aes(label=count_trips), position=position_dodge(width=0.9), vjust=-0.25) + ggtitle("Count of each User Type")

We can see that most of the riders are actual subscribers.

Using the plotly package, we can creat the trip duration per birth year of users, also showing the start and end station. Since the chart is heavy for the amout of data, we will use a 10k sample of the data.

set.seed(1)
sample_df <- df[sample(nrow(df), 10000), ]
plot_ly(data = sample_df, x = sample_df$Trip.Duration , y = sample_df$Birth.Year,
        marker = list(size = 10,
                       color = 'rgba(255, 182, 193, .9)',
                       line = list(color = 'rgba(152, 0, 0, .8)',width = 2)),
        text = ~paste("Start Station: ", sample_df$Start.Station, '$<br>Finish Station:', sample_df$End.Station)
        ) %>%
  layout(title = 'Trip duration distribution',
         yaxis = list(zeroline = FALSE),
         xaxis = list(zeroline = FALSE))

## Warning: Ignoring 2053 observations

We can see that the majority of trips stay under the 10k seconds threshold and user age doesn’t really seem to have an impact on it. The ride with most duration departed from Clinton st and lasted for more than 74K seconds (or more than 20 hours!). It’s probably a user who forgot to return the bike =D

Since the Female riders seems to have longer trips on average on age hasn’t prove to have much different on trip duration, let’s try grouping our data by age and gender and check the average trip duration for the group:

grouped_df <- df %>%
              group_by(Birth.Year,Gender) %>%
              summarise(mean_trip_duration = mean(Trip.Duration)) %>%
              ungroup() %>%
              arrange(Birth.Year)

ggplot(aes(x = Birth.Year, y = mean_trip_duration),
       data = subset(grouped_df, Gender %in% c('Male','Female'))) +
       geom_line(aes(color = Gender), stat = 'summary', fun.y = median) + ggtitle("Mean Trip Duration per Gender and Distributed by Birth Year")

For the birthday range where we have most of the datapoints (between 1950 and 2000), we can see that Female riders consistently have a higher trip duration than Male riders.

Let’s check the same analysis, but for customers x subscribers:

grouped_df <- df %>%
              group_by(Birth.Year,User.Type) %>%
              summarise(mean_trip_duration = mean(Trip.Duration)) %>%
              ungroup() %>%
              arrange(Birth.Year)

ggplot(aes(x = Birth.Year, y = mean_trip_duration),
       data = subset(grouped_df, User.Type %in% c('Customer','Subscriber'))) +
       geom_line(aes(color = User.Type), stat = 'summary', fun.y = median) + ggtitle("Mean Trip Duration per User Type and Distributed by Birth Year")

## Warning: Removed 1 rows containing non-finite values (stat_summary).

Customers have longer trip than subscribers. Could that be a consequence of more female riders as customers than subscribers?

3.6 Stations

Finally, let’s check which are the 5 most popular stations on both starting and finishing rides:

grouped_start <- df %>%
                 group_by(Start.Station) %>%
                 summarise (no_rows = length(Start.Station))

grouped_start <- grouped_start [order (-grouped_start$no_rows),] %>%
                 mutate (Pareto = cumsum(grouped_start$no_rows/sum(grouped_start$no_rows)))
                 
grouped_start <- head(grouped_start,5)

grouped_end <- df %>%
                 group_by(End.Station) %>%
                 summarise (no_rows = length(End.Station))

grouped_end <- grouped_end [order (-grouped_end$no_rows),] %>%
               mutate (Pareto = cumsum(grouped_end$no_rows/sum(grouped_end$no_rows)))
               
grouped_end <- head(grouped_end,5)

end_point = 0.5 + nrow(grouped_start) + nrow(grouped_start)-1

barplot(grouped_start$no_rows,  
             width = 1, space = 1.0, border = NA, axes = F,
             ylim = c(0, 1.05 * max(grouped_start$no_rows, na.rm = T)), 
             ylab = "Cummulative Counts" , cex.names = 0.7, 
             main = "5 Most Popular Start Stations")

text(seq(1.5,end_point,by=2), par("usr")[3]-0.25, 
     srt = 45, adj= 1, xpd = TRUE,labels = paste(grouped_start$Start.Station), cex=0.65)

barplot(grouped_end$no_rows,  
             width = 1, space = 1.0, border = NA, axes = F,
             ylim = c(0, 1.05 * max(grouped_end$no_rows, na.rm = T)), 
             ylab = "Cummulative Counts" , cex.names = 0.7, las=2, 
             main = "5 Most Popular End Stations")

text(seq(1.5,end_point,by=2), par("usr")[3]-0.25, 
     srt = 45, adj= 1, xpd = TRUE,labels = paste(grouped_end$End.Station), cex=0.65)

Interesting! The 2 most popular start stations are also the 2 most popular end stations. Also, both Clinton St & Madison St and Lake Shore Dr & Monroe St appear in the top 5, although in different positions.

Let’s check how many (if any) trips start and finish in the same station by adding a new column to our dataset:

df <- df %>%
      mutate (Same.Station = identical(df$End.Station,df$Start.Station))
sum(df$Same.Station)

## [1] 0

There are not trips starting and ending at the same station.